Dynamic Request Scheduling for Remote Computing Efficiency
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Solution Overview
Problem
Remote computing devices face challenges with limited computational and power resources, leading to strain when generating responses to server requests, especially when these requests are sent at predetermined intervals regardless of environmental changes.
Innovation Solution
A method and system that generate a profile for applications on remote computing devices, determining optimal times for request transmission based on feedback from the devices, and using triggers to manage resource usage, thereby reducing the frequency and improving the efficiency of message delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the server requests information from the remote computing device at predetermined intervals, then the server can maintain updated information about the device, but the remote computing device's limited computational and power resources are strained
Solution Approach 1:
The patent implements dynamic request scheduling where the server adjusts the timing and frequency of information requests based on the remote device's current state, application activity, and environmental conditions. Instead of fixed predetermined intervals, the system dynamically modifies request patterns to match actual needs, reducing unnecessary power consumption while maintaining information reliability.
Solution Approach 2:
The system incorporates feedback mechanisms where the remote device provides information about its state, application activity, and environmental conditions. The server uses this feedback to intelligently determine when information updates are actually needed, adjusting request timing based on real-world conditions rather than rigid schedules, thereby reducing overall power consumption.
2Device complexity
If the server sends requests at fixed intervals regardless of environmental changes, then the request timing is simple to manage, but the remote computing device generates responses irrespective of actual environmental changes, wasting resources
Solution Approach 1:
The system performs preliminary actions by having the remote device monitor and report environmental conditions and application state proactively. The server uses this advance information to schedule requests only when environmentally relevant, avoiding unnecessary responses. The device prepares environmental data in advance, enabling intelligent request timing without complex real-time decisioning.
3Measurement precision
If the server transmits frequent requests for application activity updates, then the server maintains accurate application state information, but the computational resources of the remote computing device are consumed
Solution Approach 1:
The system changes the parameters of request transmission by adjusting frequency and timing based on application activity patterns and environmental conditions. When applications are stable or environmental conditions indicate low relevance, request frequency is reduced. When changes are detected or conditions warrant monitoring, frequency increases, optimizing the balance between accuracy and computational efficiency.
Data Source
AI summary
The present disclosure discusses system and methods for improving the efficiency of a remote computing device. The system and methods include generate a profile and delivery schedule for the remote computing device. The system can dynamically update the delivery schedule of future requests the system transmits to the remote computing device based on responses to current request.